How are retailers creating personalized customer experiences and improving the data analytics for deeper business insights?
Predictive analytics is the usage of information, statistical algorithms, and machine learning techniques to distinguish the probability of subsequent results. The motive is to study the former information and recognize trends in the future.
Predictive analytics amalgamates this huge inflow of data with past records to forecast behavior, performance, and patterns for the future. Smart Revenue forecasting is also one use case of predictive analytics. It has proved to be a dominant asset for the retailers and is now being widely used throughout the world to maintain an edge over the competition and gain considerable market share. Predictive Analytics is solely data-based science that directs a multi-billion dollar market today.
It’s a turning point for such an industry that has spent decades gathering information on its customers from almost every point.
Understanding customer behavior and combining it with consumer demography is the first step in the deployment of predictive analytics. Retailers can use it to give targeted and highly customized offers for specific shoppers.
Before data analytics became mainstream, the option of targeted offers was non-existent or was only for large swathes of customers having one or two common characteristics. But with the emergence of online shopping, and then data analytics, it is now possible to track behavior across channels, i.e. monitor a shopper who researches in the digital store and then goes ahead and purchases the item in the physical store.
Such insights along with predictive analytics give retailers the option to make highly personalized offers to customers at a very granular level. For example, retailers can personalize the in-store experience by giving offers to incentivize frequent buying to drive more purchases, thereby achieving higher sales across all channels.
Predictive analytics can be used to cross-sell or upsell. For instance, based on her previous buying history, we know Caroline has a fondness for buying brand X of coffee at the start of every month. Using predictive analytics, a retailer can now offer Caroline a buy two get one free deal on coffee. Considering how consistent her buying behavior is, Caroline will likely take advantage of this coupon, leading to more profit for the company.
Technology that tracks people has now made it possible for retailers to find ways of analyzing in-store or online shopping behavior, and assess the impact of merchandising efforts.
Various consumer interaction points including e-commerce sites, credit card swipes (transaction), social media, and so on can provide access to diverse (and complex) data about their customers. Using these data points retailers can now build predictive models to link past behavior and demographics. The objective of such models is to score every customer according to the likelihood of purchasing certain products.
This entire data-based process also gives retailers invaluable insights into recognizing their high-value customers, establishing the customer lifetime value (CLV), a customer’s motives behind a purchase, the preferred channels, the buying patterns, etc. Retailers armed with such knowledge can not only throw up personalized offers but also retain new customers by loyalty programs that encourage them to buy from you over the competition.
An impoverished inventory is every retailer’s worst nightmare. Not only does it lead to a loss in sales over time, but also represents a poor indicator of demand for a product. Questions like what to remove, what to store, and when to do so can all be answered. Every retailer wants to keep replenished stocks of items that are popular with consumers, none wants to hang on to products that are not yielding any sales. The need to buy and discard stocks of products on a hunch is removed by Predictive analytics.
Recommendation engines work as a quick solution to increase traffic and conversions. Let’s say if a person purchases a mobile phone then the recommendation system shows us mobile covers or memory cards as items frequently bought together. There are several types of recommendations in any e-commerce portal. Netflix’s recommendation system is considered one of the best for the viewers.
Walmart’s Success Story
American multinational retail giant Walmart operates approximately 10,500 stores and clubs in 24 countries and eCommerce websites. It has complete consumer data of close to 145 million Americans. Predictive analytics is at the heart of the supply chain process that aids Walmart to reduce overstock and stay proper supply on the most in-demand products. Suppliers to Walmart are required to use the dynamic vendor inventory management system that helps them minimize the inventory for a particular product accordingly. Retailers save funds for purchasing products that are in high demand and in turn result in greater profits. Walmart observed a significant increase in online sales from 10% to 15% for $1 billion in incremental revenue.
Walmart's big data algorithms analyze credit card purchases to provide specialized recommendations to its customers based on their purchase history.
Walmart is leveraging big data analysis to build predictive capabilities on their mobile app which generates a shopping list by analyzing the data of the customers and other purchases per week. Walmart’s mobile app consists of a shopping list that can tell customers the position of their wants and helps them by providing discounts to similar products on the Walmart portal.
Another way in which Walmart is harnessing the power of big data analysis is by leveraging analytics in real-time when a customer actually enters the Walmart store. The geofencing feature of Walmart’s mobile app senses whenever a user enters the Walmart store in the US. The app asks the user to enter into the “Store Mode''. The store mode of the mobile app helps users to scan QR codes for special discounts and offers on products they would like to buy.
With user-focused design, Walmart is able to create personalised customer experiences and improve data analytics for deeper business insights.
Predictive Analysis is of immense assistance to the retail business as it motivates them to encompass and recognize their customers' needs. The above-mentioned tactics are most commonly used by retailers across big brands with effective results. These patterns or trends are being utilized to reinvent a retail store: one that is more productive, focused, and considerate towards better customer experience and that empowers brand dependability.
Shriya Madan, an ardent learner, a digital enthusiast, aspires to make an impact in the real world with the power of technology
Pranjali Apurva, driven by curiosity, converging design principles with digital transformation